Multi-constrained pipes conveying fluid,such as aircraft hydraulic control pipes,are susceptible to resonance fatigue in harsh vibration environments,which may lead to system failure and even catastrophic accidents.In...Multi-constrained pipes conveying fluid,such as aircraft hydraulic control pipes,are susceptible to resonance fatigue in harsh vibration environments,which may lead to system failure and even catastrophic accidents.In this study,a machine learning(ML)-assisted weak vibration design method under harsh environmental excitations is proposed.The dynamic model of a typical pipe is developed using the absolute nodal coordinate formulation(ANCF)to determine its vibrational characteristics.With the harsh vibration environments as the preserved frequency band(PFB),the safety design is defined by comparing the natural frequency with the PFB.By analyzing the safety design of pipes with different constraint parameters,the dataset of the absolute safety length and the absolute resonance length of the pipe is obtained.This dataset is then utilized to develop genetic programming(GP)algorithm-based ML models capable of producing explicit mathematical expressions of the pipe's absolute safety length and absolute resonance length with the location,stiffness,and total number of retaining clips as design variables.The proposed ML models effectively bridge the dataset with the prediction results.Thus,the ML model is utilized to stagger the natural frequency,and the PFB is utilized to achieve the weak vibration design.The findings of the present study provide valuable insights into the practical application of weak vibration design.展开更多
In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellu...In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.展开更多
Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such ...Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.展开更多
Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations betw...Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.展开更多
In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential param...In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters.The data used for model development was recorded by an in-situ experiment.The image processing technology is used to quantify several tree canopy parameters.Based on four accuracy metrics,i.e.root mean square error(RMSE),mean absolute percentage error(MAPE),coefficient of determination(R2),and relative error,the performance of the proposed GP model was evaluated.The results indicate that the model can give a reasonable estimation for the spatiotemporal variations of soil suction around a tree with acceptable errors.Global sensitivity analysis for the statistical model obtained using limited data of a specific region demonstrates the drying time as the most influential variable and the initial soil suction as the second most influential variable for the soil suction variations.A case study was conducted using a set of assumed input variable values and validated that the simplified GP model can be used to estimate and predict the spatiotemporal variations of soil suction in rooted soil at a certain range.展开更多
In this work, we explore and study the implication of having more than one output on a genetic programming （GP） graph-representation. This approach, called multiple interactive outputs in a single tree （MIOST）, is...In this work, we explore and study the implication of having more than one output on a genetic programming （GP） graph-representation. This approach, called multiple interactive outputs in a single tree （MIOST）, is based on two ideas. First, we defined an approach, called interactivity within an individual （IWI）, which is based on a graph-GP representation. Second, we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this, we have MIOST. As a first step, we analyze the effects of IWI by using only mutations and analyze its implications （i.e., presence of neutrality）. Then, we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process. Finally, we tested the effectiveness of MIOST by using mutations and crossover and conducted extensive empirical results on different evolvable problems of different complexity taken from the literature. The results reported in this paper indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions.展开更多
This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a prev...This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules （EDR） to detect financial opportunities. The objective of EDR is to classify the minority class （positive eases） in imbalaneed environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are： 1） to show that EDR produces a range of solutions to suit the investor＇s preferences and 2） to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive eases in imbalanced data sets. Experimental results show that： 1） EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2） a bigger collection of rules is able to classify more positive eases in imbalanced environments.展开更多
This paper presents an efficient method for globally optimizing and automating component sizing for rotary traveling wave oscillator arrays. The lumped equivalent model of transmission lines loaded by inverter pairs i...This paper presents an efficient method for globally optimizing and automating component sizing for rotary traveling wave oscillator arrays. The lumped equivalent model of transmission lines loaded by inverter pairs is evaluated and posynomial functions for oscillation frequency, power dissipation, phase noise, etc. are formulated using transmission line theory. The re- sulting design problem can be posed as a geometric programJning problem, which can be efficiently solved with a convex opti- mization solver. The proposed method can compute the global optima more efficiently than the traditional iterative scheme and various design problems can be solved with the same circuit model. The globally optimal trade-off curves between competing objectives are also computed to carry out robust designs and quickly explore the design space.展开更多
Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour ...Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R<sup>2</sup> = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.展开更多
Biomedical ontologies encapsulate the vast knowledge within the medical domain,facilitating communication and data exchange.However,the heterogeneity of these ontologies often impedes knowledge exchange,especially in ...Biomedical ontologies encapsulate the vast knowledge within the medical domain,facilitating communication and data exchange.However,the heterogeneity of these ontologies often impedes knowledge exchange,especially in large-scale biomedical ontologies.Biomedical Ontology Matching(BOM)based on partitioning addresses this issue by dividing extensive ontologies into manageable sub-ontologies and identifying equivalence relationships among heterogeneous entities.Recently,Genetic Programming(GP)has been widely employed as an effective technique for optimizing and combining ontology Similarity Features(SFs).Nevertheless,the traditional GP methods struggle with the matching tasks due to the numerous and complex SFs of the partitioned sub-ontologies.To tackle these challenges,this paper proposes an efficient multi-task matching model to solve large-scale BOM problems.Firstly,an anchor-based partitioning method is introduced,which reduces the search space while retaining more informative sub-ontologies,ensuring high-quality subsequent matching.Secondly,a novel Self-Learning Compact MultiTask Genetic Programming(SL-CMTGP)method is proposed for constructing entity SFs.This method autonomously explores correlations among different matching tasks and leverages an implicit knowledge transfer mechanism to perform evolutionary operations,significantly enhancing BOM matching quality while reducing computational complexity.Lastly,a new approximate evaluation metric is introduced to improve the guidance of evolutionary algorithms,addressing the bias problem and overcoming local optima in individual tasks.Experimental evaluations are conducted on six test cases from the Anatomy,Large Biomedical Ontologies,and Disease and Phenotype Tracks of the Ontology Alignment Evaluation Initiative(OAEI).The results demonstrate that the proposed method consistently achieves high-quality matching outcomes and significantly improves BOM efficiency across different test cases.展开更多
Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the comm...Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the community of fluid mechanics,especially in the field of active flow control(AFC).This article surveys recent successful applications of machine learning in AFC,highlights general ideas,and aims at offering a basic outline for those who are interested in this specific topic.In this short review,we focus on two methodologies,i.e.,genetic programming(GP)and deep reinforcement learning(DRL),both having been proven effective,efficient,and robust in certain AFC problems,and outline some future prospects that might shed some light for relevant studies.展开更多
Researchers in the past had noticed that application of Artificial Neural Networks （ANN） in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predict...Researchers in the past had noticed that application of Artificial Neural Networks （ANN） in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming （GP） has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.展开更多
A dynamic simulation method for non-linear systems based on genetic programming （GP） and bond graphs （BG） was developed to improve the design of nonlinear multi-domain energy conversion systems. The genetic operat...A dynamic simulation method for non-linear systems based on genetic programming （GP） and bond graphs （BG） was developed to improve the design of nonlinear multi-domain energy conversion systems. The genetic operators enable the embryo bond graph to evolve towards the target graph according to the fitness function. Better simulation requires analysis of the optimization of the eigenvalue and the filter circuit evolution. The open topological design and optimized convergence for the operation, but also the design of nonlinear multi-domain systems. space search ability of this method not only gives a more reduces the generation time for the new circuit graph for展开更多
基金Project supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.12421002)the National Science Funds for Distinguished Young Scholars of China(No.12025204)+1 种基金the National Natural Science Foundation of China(No.12372015)China Scholarship Council(No.202206890065)。
文摘Multi-constrained pipes conveying fluid,such as aircraft hydraulic control pipes,are susceptible to resonance fatigue in harsh vibration environments,which may lead to system failure and even catastrophic accidents.In this study,a machine learning(ML)-assisted weak vibration design method under harsh environmental excitations is proposed.The dynamic model of a typical pipe is developed using the absolute nodal coordinate formulation(ANCF)to determine its vibrational characteristics.With the harsh vibration environments as the preserved frequency band(PFB),the safety design is defined by comparing the natural frequency with the PFB.By analyzing the safety design of pipes with different constraint parameters,the dataset of the absolute safety length and the absolute resonance length of the pipe is obtained.This dataset is then utilized to develop genetic programming(GP)algorithm-based ML models capable of producing explicit mathematical expressions of the pipe's absolute safety length and absolute resonance length with the location,stiffness,and total number of retaining clips as design variables.The proposed ML models effectively bridge the dataset with the prediction results.Thus,the ML model is utilized to stagger the natural frequency,and the PFB is utilized to achieve the weak vibration design.The findings of the present study provide valuable insights into the practical application of weak vibration design.
基金supported by National Natural Science Foundation of China (No.61501028)Beijing Institute of Technology Research Fund Program for Young Scholars
文摘In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.
基金supported by JKW Program(No.M102-03)National Program(No.E0F80246).
文摘Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.
文摘Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.
基金the National Key R&D Program of China(No.2019YFB1600700)the Science and Technology Development Fund of Macao(Nos.SKL-IOTSC-2018-2020 and 0193/2017/A3)the University of Macao Research Fund(No.MYRG2018-00173-FST),China。
文摘In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters.The data used for model development was recorded by an in-situ experiment.The image processing technology is used to quantify several tree canopy parameters.Based on four accuracy metrics,i.e.root mean square error(RMSE),mean absolute percentage error(MAPE),coefficient of determination(R2),and relative error,the performance of the proposed GP model was evaluated.The results indicate that the model can give a reasonable estimation for the spatiotemporal variations of soil suction around a tree with acceptable errors.Global sensitivity analysis for the statistical model obtained using limited data of a specific region demonstrates the drying time as the most influential variable and the initial soil suction as the second most influential variable for the soil suction variations.A case study was conducted using a set of assumed input variable values and validated that the simplified GP model can be used to estimate and predict the spatiotemporal variations of soil suction in rooted soil at a certain range.
基金This paper was supported by the Mexican Consejo Nacional de Ciencia y Tecnologia(CONACyT)for the postgraduate studies at University of Essex.
文摘In this work, we explore and study the implication of having more than one output on a genetic programming （GP） graph-representation. This approach, called multiple interactive outputs in a single tree （MIOST）, is based on two ideas. First, we defined an approach, called interactivity within an individual （IWI）, which is based on a graph-GP representation. Second, we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this, we have MIOST. As a first step, we analyze the effects of IWI by using only mutations and analyze its implications （i.e., presence of neutrality）. Then, we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process. Finally, we tested the effectiveness of MIOST by using mutations and crossover and conducted extensive empirical results on different evolvable problems of different complexity taken from the literature. The results reported in this paper indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions.
文摘This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules （EDR） to detect financial opportunities. The objective of EDR is to classify the minority class （positive eases） in imbalaneed environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are： 1） to show that EDR produces a range of solutions to suit the investor＇s preferences and 2） to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive eases in imbalanced data sets. Experimental results show that： 1） EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2） a bigger collection of rules is able to classify more positive eases in imbalanced environments.
基金Project (No 20060335065) supported by the Specialized Research Fund for the Doctoral Program of Higher Education of Ministry of Education, China
文摘This paper presents an efficient method for globally optimizing and automating component sizing for rotary traveling wave oscillator arrays. The lumped equivalent model of transmission lines loaded by inverter pairs is evaluated and posynomial functions for oscillation frequency, power dissipation, phase noise, etc. are formulated using transmission line theory. The re- sulting design problem can be posed as a geometric programJning problem, which can be efficiently solved with a convex opti- mization solver. The proposed method can compute the global optima more efficiently than the traditional iterative scheme and various design problems can be solved with the same circuit model. The globally optimal trade-off curves between competing objectives are also computed to carry out robust designs and quickly explore the design space.
文摘Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R<sup>2</sup> = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.
基金supported by the National Natural Science Foundation of China(No.62172095)the Research Project of Shanxi Scholarship Council of China(No.2023061).
文摘Biomedical ontologies encapsulate the vast knowledge within the medical domain,facilitating communication and data exchange.However,the heterogeneity of these ontologies often impedes knowledge exchange,especially in large-scale biomedical ontologies.Biomedical Ontology Matching(BOM)based on partitioning addresses this issue by dividing extensive ontologies into manageable sub-ontologies and identifying equivalence relationships among heterogeneous entities.Recently,Genetic Programming(GP)has been widely employed as an effective technique for optimizing and combining ontology Similarity Features(SFs).Nevertheless,the traditional GP methods struggle with the matching tasks due to the numerous and complex SFs of the partitioned sub-ontologies.To tackle these challenges,this paper proposes an efficient multi-task matching model to solve large-scale BOM problems.Firstly,an anchor-based partitioning method is introduced,which reduces the search space while retaining more informative sub-ontologies,ensuring high-quality subsequent matching.Secondly,a novel Self-Learning Compact MultiTask Genetic Programming(SL-CMTGP)method is proposed for constructing entity SFs.This method autonomously explores correlations among different matching tasks and leverages an implicit knowledge transfer mechanism to perform evolutionary operations,significantly enhancing BOM matching quality while reducing computational complexity.Lastly,a new approximate evaluation metric is introduced to improve the guidance of evolutionary algorithms,addressing the bias problem and overcoming local optima in individual tasks.Experimental evaluations are conducted on six test cases from the Anatomy,Large Biomedical Ontologies,and Disease and Phenotype Tracks of the Ontology Alignment Evaluation Initiative(OAEI).The results demonstrate that the proposed method consistently achieves high-quality matching outcomes and significantly improves BOM efficiency across different test cases.
基金This work was support by the Research Grants Council of Hong Kong under General Research Fund(Grant Nos.15249316,15214418)the Departmental General Research Fund(Grant No.G-YBXQ).
文摘Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the community of fluid mechanics,especially in the field of active flow control(AFC).This article surveys recent successful applications of machine learning in AFC,highlights general ideas,and aims at offering a basic outline for those who are interested in this specific topic.In this short review,we focus on two methodologies,i.e.,genetic programming(GP)and deep reinforcement learning(DRL),both having been proven effective,efficient,and robust in certain AFC problems,and outline some future prospects that might shed some light for relevant studies.
基金University Sains Malaysia for funding a short term grant (304.PREDAC.6035262) to conduct this on-going research
文摘Researchers in the past had noticed that application of Artificial Neural Networks （ANN） in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming （GP） has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.
基金Supported by the Basic and Frontier Technology Research Program of Henan Province (No.082300410390)Backbone of Young Teachers in University Plan to Subsidize Projects of Henan Province (No.2005-174)
文摘A dynamic simulation method for non-linear systems based on genetic programming （GP） and bond graphs （BG） was developed to improve the design of nonlinear multi-domain energy conversion systems. The genetic operators enable the embryo bond graph to evolve towards the target graph according to the fitness function. Better simulation requires analysis of the optimization of the eigenvalue and the filter circuit evolution. The open topological design and optimized convergence for the operation, but also the design of nonlinear multi-domain systems. space search ability of this method not only gives a more reduces the generation time for the new circuit graph for
